We present an algorithm for compressing 2D vector fields that preserves topology. Our approach is to simplify the given data set using constrained clustering. We employ different...
Suresh K. Lodha, Jose C. Renteria, Krishna M. Rosk...
In this paper, we propose a viewer for huge point-sampled models by combining out-of-core technologies with view-dependent level-of-detail (LOD) control. This viewer is designed o...
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
The k-means algorithm is widely used for clustering, compressing, and summarizing vector data. In this paper, we propose a new acceleration for exact k-means that gives the same a...
Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for correct clustering. Among several existing measures of topology violation, this paper studies t...